World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
53
Citations
12212
World Ranking
4792
National Ranking
2230

Hans Peter Graf publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Hans Peter Graf sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 142 publications — 23rd percentile

23% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Hans Peter Graf D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Hans Peter Graf sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 53 D-Index — 67th percentile

67% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Hans Peter Graf is affiliated with NEC in the United States and has contributed to research primarily in the field of Computer Science. Their work encompasses several subfields, notably Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Biophysics, and Signal Processing.

Their research topics include:

  • Human Pose and Action Recognition
  • Multimodal Machine Learning Applications
  • Video Analysis and Summarization
  • Anomaly Detection Techniques and Applications
  • AI in cancer detection
  • Radiomics and Machine Learning in Medical Imaging
  • Cell Image Analysis Techniques

Hans Peter Graf has published papers in various venues, including:

  • arXiv (Cornell University)
  • The Journal of Pathology Clinical Research
  • Journal of Physics Conference Series

Recent papers reflect an emphasis on applications of artificial intelligence and machine learning in both medical and computer vision domains:

  • "Development of multiple AI pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&E-stained tissues" (2023), The Journal of Pathology Clinical Research
  • "S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation" (2020), arXiv (Cornell University)
  • "Hopper: Multi-hop Transformer for Spatiotemporal Reasoning" (2021), arXiv (Cornell University)
  • "COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality" (2021), arXiv (Cornell University)
  • "Learning Higher-order Object Interactions for Keypoint-based Video Understanding" (2023), arXiv (Cornell University)

The researcher collaborates frequently with several co-authors who contribute to topics in related fields. Frequent collaborators include:

  • Asim Kadav
  • Farley Lai
  • Honglu Zhou
  • Mubbasir Kapadia
  • Aviv Shamsian

Overall, Hans Peter Graf's body of work focuses extensively on computer vision and machine learning techniques, particularly their use in medical imaging and human activity recognition contexts.

Best Publications

  • Pruning Filters for Efficient ConvNets

    Hao Li;Asim Kadav;Igor Durdanovic;Hanan Samet

  • Handwritten digit recognition: applications of neural network chips and automatic learning

    Y. Le Cun;L.D. Jackel;B. Boser;J.S. Denker

  • Parallel Support Vector Machines: The Cascade SVM

    Hans P. Graf;Eric Cosatto;Léon Bottou;Igor Dourdanovic

  • Massively parallel processing core with plural chains of processing elements and respective smart memory storing select data received from each chain

    Srihari Cadambi;Abhinandan Majumdar;Michela Becchi;Srimat Chakradhar

  • An image transform approach for HMM based automatic lipreading

    G. Potamianos;H.P. Graf;E. Cosatto

  • A Massively Parallel Coprocessor for Convolutional Neural Networks

    Murugan Sankaradas;Venkata Jakkula;Srihari Cadambi;Srimat Chakradhar

  • Visual prosody: facial movements accompanying speech

    H.P. Graf;E. Cosatto;V. Strom;Fu Jie Huang

  • VLSI implementation of a neural network memory with several hundreds of neurons

    H. P. Graf;L. D. Jackel;R. E. Howard;B. Straughn

  • VLSI implementation of a neural network model

    Hans P. Graf;Lawrence D. Jackel;Wayne E. Hubbard

  • Neural Network Recognizer for Hand-Written Zip Code Digits

    John S. Denker;W. R. Gardner;Hans Peter Graf;Donnie Henderson

  • Face feature analysis for automatic lipreading and character animation

    Hans Peter Graf;Eric David Petajan

  • Multi-modal system for locating heads and faces

    H.P. Graf;E. Cosatto;D. Gibbon;M. Kocheisen

  • System and method of providing conversational visual prosody for talking heads

    Eric Cosatto;Hans Peter Graf;Thomas M. Isaacson;Volker Franz Strom

  • Photo-realistic talking-heads from image samples

    E. Cosatto;H.P. Graf

  • A reconfigurable VLSI neural network

    S. Satyanarayana;Y.P. Tsividis;H.P. Graf

  • Method and apparatus for separating static and dynamic portions of document images

    Hans P. Graf;Daniel J. Mayer

  • Discriminative training of HMM stream exponents for audio-visual speech recognition

    G. Potamianos;H.P. Graf

  • Image skeletonization method

    John S. Denker;Hans P. Graf;Donnie Henderson;Richard E. Howard

  • System and method of controlling sound in a multi-media communication application

    Joern Ostermann;Mehmat Reha Civanlar;Hans Peter Graf;Thomas M. Isaacson

  • Method for locating a subject's lips in a facial image

    Hans Peter Graf

Frequent Co-Authors

Lawrence D. Jackel
Lawrence D. Jackel Toyota Research Institute
Richard Howard
Richard Howard Rutgers, The State University of New Jersey
John S. Denker
John S. Denker Nokia (United States)
Srimat T. Chakradhar
Srimat T. Chakradhar NEC (United States)
Bernhard E. Boser
Bernhard E. Boser University of California, Berkeley
Gerasimos Potamianos
Gerasimos Potamianos University Of Thessaly
Isabelle Guyon
Isabelle Guyon University of Paris-Saclay
Yann LeCun
Yann LeCun Facebook (United States)
Yannis Tsividis
Yannis Tsividis Columbia University
Henry S. Baird
Henry S. Baird Lehigh University

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